An Extended Adaptive Large Neighbourhood Search for Vehicles’ Task Offloading in Platooning

With the arrival of the Internet of Things, many fragmented intelligent terminals unload data to the edge cloud for computing. Based on the background of the vehicle platooning assisted by the edge cloud server, this paper conducted the following research on the problem of computing task offloading: First, considering the limited heterogeneous network resources of the Internet of Vehicles, the limited computing resources allocated by the edge cloud server for the vehicle platoon and member vehicle onboard computing unit, and the different delay constraints of the computing tasks, a computation offloading model was established with the optimization objective of reducing the total energy consumption of the platooning. Second, this paper used the proposed extended adaptive large neighbourhood search (EALNS) algorithm to optimize the offloading decision of computing tasks and used a method that enables computing tasks to be completed within the time delay constraint to optimize the allocation of computing resources. Finally, the EALNS and the generalized Benders decomposition algorithm were compared for energy optimization experiments. The experimental results verified the EALNS algorithm's effectiveness in optimizing the platooning's total energy consumption in the task offloading decision-making process.